{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "---\n",
    "title: 👩‍❤️‍💋‍👨Code-4-Deep-Learning\n",
    "password: \"\"\n",
    "tags:\n",
    "  - 人工智能\n",
    "  - 深度学习\n",
    "  - python\n",
    "katex: false\n",
    "comments: true\n",
    "aside: true\n",
    "date: 2022-04-21 00:26:11\n",
    "cover: https://pan.weidows.tech/d/local/blog/RHe8xK.png\n",
    "top_img:\n",
    "---\n",
    "\n",
    "<!--\n",
    " * @?: *********************************************************************\n",
    " * @Author: Weidows\n",
    " * @LastEditors: Weidows\n",
    " * @LastEditTime: 2022-04-20 23:11:24\n",
    " * @FilePath: \\Blog-private\\scaffolds\\post.md\n",
    " * @Description:\n",
    " * @!: *********************************************************************\n",
    "-->\n",
    "\n",
    "## 序\n",
    "\n",
    "此文为其他文章的代码部分:\n",
    "\n",
    "> [⚡再啃-Deep-Learning](../../AI/DL)\n",
    "\n",
    "也提供了 notebook 形式: [代码地址](https://github.com/Weidows-projects/public-post/blob/main/notebook/DL/DL.ipynb)\n",
    "\n",
    "<a>![分割线](https://cdn.jsdelivr.net/gh/Weidows/Weidows/image/divider.png)</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 神经网络\n",
    "\n",
    "### 感知器\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 1 0\n",
      "1 0 1 0\n",
      "1 0 0 1\n"
     ]
    }
   ],
   "source": [
    "def AND(x1, x2):\n",
    "    w1, w2, theta = 0.5, 0.5, 0.7\n",
    "    tmp = x1 * w1 + x2 * w2\n",
    "    if tmp <= theta:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "\n",
    "def OR(x1, x2):\n",
    "    w1, w2, theta = 0.5, 0.5, 0.2\n",
    "    tmp = x1 * w1 + x2 * w2\n",
    "    if tmp <= theta:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "\n",
    "# 非门只取一个输入,另一个不管\n",
    "def NOT(x1, x2):\n",
    "    w1, w2, theta = -1, 0, 0\n",
    "    tmp = x1 * w1 + x2 * w2 + 1\n",
    "    return tmp\n",
    "\n",
    "\n",
    "# 异或门是非线性运算, 需要多层感知器组合\n",
    "def XOR(x1, x2):\n",
    "    # 异或门 = (与非门 与 或门)\n",
    "    return AND(OR(x1, x2), not AND(x1, x2))\n",
    "\n",
    "\n",
    "print(AND(0, 1), AND(1, 1), OR(0, 1), OR(0, 0))\n",
    "print(NOT(0, 1), NOT(1, 1), NOT(0, 0), NOT(1, 0))\n",
    "print(XOR(0, 1), XOR(1, 1), XOR(0, 0), XOR(1, 0))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<a>![分割线](https://cdn.jsdelivr.net/gh/Weidows/Weidows/image/divider.png)</a>\n",
    "\n",
    "## Pytorch\n",
    "\n",
    "### MNIST-手写数字识别\n",
    "\n",
    "入门典中典 <sup id='cite_ref-1'>[\\[1\\]](#cite_note-1)</sup>, 具体教程推荐 <sup id='cite_ref-2'>[\\[2\\]](#cite_note-2)</sup> <sup id='cite_ref-3'>[\\[3\\]](#cite_note-3)</sup>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 1, 28, 28])\n",
      "tensor([1, 2, 8, 5, 2, 6, 9, 9, 9, 4, 0, 3, 9, 9, 5, 6, 7, 8, 8, 9, 2, 6, 9, 3,\n",
      "        0, 5, 0, 7, 6, 1, 2, 0, 7, 4, 6, 0, 6, 9, 7, 0, 7, 3, 2, 5, 9, 0, 4, 8,\n",
      "        3, 6, 4, 0, 3, 2, 6, 6, 3, 2, 2, 3, 6, 7, 8, 4])\n"
     ]
    },
    {
     "data": {
      "image/png": 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0s6455TzPzKYCC82s0X6BlIPGWi9CCGNDCA+FEH4IISwi1cDu2rC/VtPQWOuEmW0BHAAMCiF8F0JYHkKYVNffI58uggGkvsE3BboCA/Pc90KgLbAEeBV4O/14DDAs5/1HAvsCnYAu6X0xsx2Ae4DBQBvgTuBxM1slY9/Dgb6ko9L0B31EHeWbYWafpz+gtnn8XtK464XbA3gvj9+rqWuMdaI7qWVwLkt3EbxjZgfX+duEEOr8AaYDR2U8vg64I709EBif8/4AdE5vjwLuynjtNOC/GY+3BebmnOvkjMe/Bj5Jb98OXJFzrg+Bnhn7Hh/nd0q/f3VgJ1JhfztSH+Czcfdv6j+NtV7kHKcrqcvB3Uv9966En8ZaJ4AL0mW9FGgB9AQWAFvWtl8+EezXGduLSDVOcX2Tsb24mse5x5qZsT0D2DC93RE4Kx3yzzWzucDGGa/n7lurEMKCEMJbIYRlIYRvgN8B+5jZmnGPIY2vXrj0petY4PQQwiv57t+ENcY6sRj4CbgyhLA0hPAS8G9gn9p2KkQf5UJgNX9gZusX4JgbZ2x3AL5Mb88ErgohXFXLvg1ZxdH3tQYcQ1Iqul6YWUdgHKko6O95lVJqUsl1Ymq+BYPCpGlNAbY2s27pDuZLC3DMIWbW3szWIRWaj04/fxdwspl1t5RWZtbXzNaoz0nSx9nCzFYyszbAzcCLIYR5BfgdmrpKrhcbAf8Cbg0h3FGAcktKxdYJ4GXgf8AfzGxlM9sV6EUq06RGDW5gQwgfAZeT+rb/GBhf+x6x/AN4Dvg0/XNl+lxvASeRuqs7B5hGHR3oZvaemR1Zw8ubAc8A84F3SXWqH97w4kuF14sTSdWNSywjR7oA5W/SKrlOhBB+Ag4k1c87j1QDfkwI4YNaj5nuwBURkQJr0iO5RESSpAZWRCQhamBFRBKiBlZEJCFqYEVEEpLXQAMzaxIpByEEDTSIqanUCWBWCGHdUheiEqhORBTBisQzo9QFkLJTZ51QAysikhDNlyoiTc6xxx4LwG233Vb13IwZqYB07733BuCrr75q8HkUwYqIJEQRrIg0es2bNwegb9++AIwcORKAd999t+o9/lohIlenCFZEJCGKYKXirLJKatWPFi1aANC/f38ANt9886z3ffTRRwDcf//9APz888/FKqKUmd69U6t7P/zwwwAsWbIEgIMOOqjqPTNn5j0ne50UwYqIJKTkEezQoUOBqI/kxhtvLGVxpIy0atUKgDXXTK3g07NnTwDOPfdcALp27Vr9jjn22msvAC644IKq5wrZzyblyyPXBx54IOv5o48+GoBp06Ylen5FsCIiCSlZBLvqqqsCMGTIEABGjBhRqqJImfGI1fvL9txzTwDMUiOYfZL4n376CYjyF1u2bAlA+/bts47n0cpOO+1U9dw++6TWqlMkW3xt2rQBYMcddwRg+vTpQNRnXshzXHPNNUDUb+/tzJgxYwp2rtooghURSUjJItizzz4bWPHOr8ipp54KRJGr+/LL1IKhf/97apHXl19+GYBnnnkGgLZt2wLQq1cvAM4880wAunfvDsCWW25ZdazLLrsMgEGDBhW8/FK7NdZIrTu47bbbAtEd/UJEsGuvvTYAzz6bWotw++23B6JMklNOOaXB58iHIlgRkYSogRURSUheq8oWYp7HDTfcEIDXXnsNiG5I+I2Kum46vPjii0CUZO6TNSxduhSAzz//vKFF1HyweUhi7s9x48YB0aW+J4B7ys0nn3wS6zjexfD4448D0Y3VTCuvHLuXbFIIYae63yalmA/WU/q86+f3v/89AJMnTwZg1113BWDx4sWFPG2ddUIRrIhIQooewfqNh0IPKPjuu+8AOPjggwEYP358vY+lCDa+JKKVdu3aAdENUI9g/SonXx9++CEAnTp1WuE1RbCFV4oI9tZbbwXg5JNPBuDtt98G4JBDDgHqX3fqoAhWRKRUip6mdeihh1b7/BdffAFEfSSdO3cGYO7cuQB89tlnsY6/xRZbAA2LYKW0vvnmm6x/RXL5QAJPx9phhx0AmDBhAhBNqJ1Q5BqbIlgRkYSUfLIXd9NNNwHw0EMPAVGk+/HHHwPRnWCRuDbZZBMgSmzPdPvttxe5NFJIuQMJPFI96aSTAPj0009LU7AcimBFRBJSNhHsJZdcAkRDaHP5MMe33nqr2tffeOMNAObPn59A6aQYDj/8cCCaXnC99dYDorzYW265Ja/j+R1lP06mF154od7llOJr1qwZEE3W4n2uHrn6ci8ffPBBCUpXM0WwIiIJKZsIdvXVV8/6N9fVV19d6/533HEHEE0UIpXj0ksvBaIJsVdaKft736MTn3LuhhtuqPV4PhWhj+Zxmf2ujz32WP0LLEXnkevAgQOznvfP+P333y92kWJRBCsikpCij+Tq0qULUHdfyaRJk4Ca89j22GMPIJqiLncSZs+Hre0YNdFIrvjyrRPel3bRRRdVPeeRq4/G+fbbb4FoQmZf1NAjW19mKLdP1o/t0xj26NEDiOpS5vSHCxcuzKfYoJFcsRVyJNfll18OwIUXXpj1vF+leP98rtxJvX0ydh/p6dMXvvTSS1X7+LSJedBILhGRUil6H6yP2PJvkpr85z//AaLlJHLttttuAKy77rpANLeB5z768iJSHnzMv0ervnAhwOmnnw7UnJvqy3t4JOvRy9133w3AokWLALj44ouBaIJtf/66664D6hW1Son4la7PXeJXqF5/ciNXzxQ58sgjgSjibd26dbXH92WEvH8fYOzYsYUoehZFsCIiCSl6H2xSfPacbt26AXD88cdXvTZq1Ki8jqU+2PjqqhO5ket5550HZEew3vdaE1+e269qvM7+8Y9/zHrsC2j6siEe4XoU1EDqg42pEO3E888/D0Q50c899xwAAwYMAKJ8dx+lN3z4cACOOuooIJoH1q9+nnrqKSDKn73nnnsAOO2006rOWVc9rIb6YEVESqXiI1jPm3311VcB2HrrrbMeQzSbeVyKYOOrq074InOeu+oRrEcc+fBFD6sbmZXJr1hOPPHEvM9RC0WwMdW3nfAVKyCKWGfPng1Ecw743MB+D8bzYzfeeGMguhfjudXOV7PwlVR8wcUNNtig6j31mL1NEayISKmUzUiufHnk+sADDwBR5OqefPLJopdJIh4xnHPOOQBMnDgRqF/k6rxfzu8U53rvvfeyzimVJfNz8ywg7yP1yNVn2Rs9ejQAr7/+OhBdpc6ZMyfrmB06dADg4YcfBqLI1eepSHrOYUWwIiIJqdgI1kfxeB6bf+N5n7JHtlIaPgLHIwh/HEfPnj2BaLSe9815v1td9w2UA12ZMj9X3/bMEc8WuPbaawH46KOPgGjNLc+b9fx671vdb7/9AFiwYEHW648++mhCv0U2RbAiIgmpmAi2efPmQDRap1evXlmv+zeej/DwtbykNH79619nPfYx4dWNpvLcxfbt2wPR3L8tWrTIet+7774LRDMn7bzzzkA0es/74XP7e72fzVfHgChPUsqH57JDNFrP+939/3PHjh2BKCL1eX19BWLnn++wYcOAKLPkq6++KnzBa6EIVkQkIWWfB+szIp111llAzXMY+Dhiv8PckAhWebDx1VQnPC8x7mrA1fG7yk888QQA33//PRDdKfZ82GOOOQaI7jhvtNFG1R5v3rx5K5TP5yuIQXmwMRWinfA8Vv//vtVWW2W9ft999wHRlauP1PL/9x75Jkx5sCIipaIGVkQkISXrIujTpw8A/fr1A+CRRx4BopScNddcE4DBgwcDKy4j4ryT2xfMmzVrVoPLpi6C+GqqE/55eZfNoEGDgOoXrfRuBE8ed/5ZLl++PFZZfFKYyy67DID9998/6/UpU6ZUbe+yyy5AXpMsq4sgpnIcUp8QdRGIiJRKySJYXzLEo40ff/wRiBa2y00W9xQOT7O46qqrgCgqWbx4caGKpgg2D3HrhKdcLV26NNHyQDRFotclt2zZsqrtJJYHkRRFsBFFsCIiCSnZQAMfquZRhUekzqch80XJxo8fD0STPkhlKUbk6rxOZUasIqWgCFZEJCFlP9CgFNQHG19TqROoDzY21YmIIlgRkYSogRURSYgaWBGRhKiBFRFJiBpYEZGE5JsHOwuYkURBykjHUhegwjSFOgGqF/lQnUjLK01LRETiUxeBiEhC1MCKiCREDayISELUwIqIJEQNrIhIQtTAiogkRA2siEhC1MCKiCREDayISELUwIqIJEQNrIhIQtTAiogkRA2siEhCyq6BNbPpZta71OWQ8qJ6IbkqoU6UXQNbbGZ2oplNM7MFZvaMmW1Y6jJJ6aleSK761IlG28CaWZ2TiZtZT+BPwIHAOsBnwAMJF01KSPVCciVZJ2I1sOlQ/Gwzm2pm88xstJm1TL820MzG57w/mFnn9PYoM7vNzMamW/4JZra+md1kZnPM7AMz2z7nlDub2fvp10f6udLH62dmk81srplNNLOuOeU8z8ymAgtj/OH2Bx4KIbwXQlgKXAHsYWad4vxdmjrVC8mlOpEtnwh2ALAfsCnQFRiY574XAm2BJcCrwNvpx2OAYTnvPxLYF+gEdEnvi5ntANwDDAbaAHcCj5vZKhn7Hg70BVqHEJalP+gjaiiXpX8yHwNsk8fv1tSpXkgu1Ym0fBrYm0MIX4YQZgNPAN3y2PfREMKkEMKPwKPAjyGEv4UQlgOjgdxvpeEhhJnpc11F6g8BcBJwZwjh9RDC8hDCvaQ+hB455ZwZQlgMEELoGkL4Rw3lehoYYGZdzWxV4GIgAKvl8bs1daoXkkt1Ii2fBvbrjO1FwOp57PtNxvbiah7nHmtmxvYMwDuTOwJnpUP+uWY2F9g44/XcfWsVQngBuAR4OH2e6cB84PO4xxDVC1mB6kRaIW5yLSSjFTez9QtwzI0ztjsAX6a3ZwJXhRBaZ/ysFkLI7GzOaxXHEMKtIYTNQwjrkfrjrQy825DCC6B6IStqcnWiEA3sFGBrM+uW7mC+tADHHGJm7c1sHeACUpcGAHcBJ5tZd0tpZWZ9zWyN+pzEzFqa2TbpY3UARgB/CSHMKcDv0NSpXkiuJlcnGtzAhhA+Ai4HxgEfA+Nr3yOWfwDPAZ+mf65Mn+stUn0rw4E5wDTq6EA3s/fM7MgaXm6ZPtcC4A1SHeoXNbz4onohuZpinbAQ8oqSRUQkpkY70EBEpNTUwIqIJEQNrIhIQtTAiogkRA2siEhC6pxFJpOZNYmUgxCC1f0ugaZTJ4BZIYR1S12ISqA6EVEEKxLPjFIXQMpOnXVCDayISELUwIqIJEQNrIhIQtTAiogkRA2siEhC1MCKiCREDayISELUwIqIJEQNrIhIQvIaKltMu+22GwDnnnsuAPvvvz8APkH4hAkTAHjiiScAuPHGGwFYvnx5Ucsphde8eXMAjjrqKAB69uwJQP/+/QF49dVXAfjmm9R6ePPmzQPgtttuA+CDDz4oXmGlqDp06ADAddddB8BBBx0EwMorp5qy5557DoDhw4cD8OSTTxa7iFkUwYqIJCSvJWOKMYnDTjvtBMCDDz4IwCabbBJrv9tvvx2AIUOGNLgMmuwlvkLWiZYtWwLRZ3nsscfmtf/bb78NwNChQwGYOHFioYoGMCmEsFMhD9hYJdFOHH300QAMGzYMgHXWWQeA7777DoCHH34YiK5011tvPQC23nprAD755JNCFwli1AlFsCIiCVEDKyKSkJJ3EbRq1QqAQw45BIg6p/155zcymjVrBsDcuXMBaN++PQA//fQTAAcccAAAzz77bL3LpC6C+ApRJ/wz/eMf/wjApZdemvX6jBmpWeHGjh2b9bzfCN1iiy2A6OaY39jwulAg6iKIqZDtRI8ePQB4+umnAVhrrbUAuPvuuwE47bTTAFiyZEnW+70d+d3vfgfAa6+9VqgiZVIXgYhIqZQ8Tevmm28GYODAgQCYpYJHT796/PHHARg1alTWfh55b7bZZkB0g8O/sRoSwUpx9enTB4gi16VLlwIwYsQIAM4880yg5hS8XXbZBYjqjFQ+T7vyz75169ZA9P/8lFNOAVasEx6p7rnnngAcfPDBAKyxxhoAPP/88wmWekWKYEVEElKyCLZbt24ADBgwAIDPPvsMiL6xxo0bB8DixYuz9vPk83fffReAyZMnAzBz5sxEyyvJ8dQ8/6yvvPJKAK6++upY+3s6lqfweN2QyrXBBhsA0b0Zd8UVVwB1Dyjyq5pbbrkFgNmzZwPQsWNHAE4//XQA7rzzzqp9fvzxx4YWewWKYEVEElL0CHabbbYB4KmnngKiqMX7TGqKRH2/kSNHAlHE26VLl1r3k/Lkd/wBDjzwQAA+/PBDIH7kmuuqq67Keuz9eJ5pcsYZZ1S95ldK+WTRSPH5PRn/d9KkSbW+v02bNgCcf/75QJSN5FfKzvv9fYACwOeff16AEmdTBCsikpCiRLCrrLJK1bb3iXgfy3777QfUHYH6HWbPmezcuTMQ5UJ6tPLoo48WqNSSJP/cAbbbbjsApk+fDkR3jD3XOV8bbrghABdddBEAgwcPXuE9f/jDH4AV+/ilvOReYXh/vQ+R9eHVnvN88cUXA1F20bRp0wB44YUXaj1uUhTBiogkpCgR7HHHHVe17VPPPfLII0DdeWlrrrkmAH379q32dY9oPZ82d7SPlKd//etfVdvff/89EE3s4xkkb775Zl7H9H619ddfH4AWLVpkvf7MM89UbfvIPylP3377LQBjxowB4NBDDwWiPlMf3edZAc77aj1C9aktPbe62BTBiogkpCgRrPeTACxbtgyACy+8EKi7L2TQoEFAdj9udXzEl1SGhQsXVm37Z+yj9XbYYYesf+vi81S89dZbQBS1/Pa3vwXg5ZdfBqIoCKJ6KOXJ5xbwfFWPaL2v1a9cPRvpvffeA6K+/a5duwKlvyejCFZEJCFFiWD79etXtf3+++8DdS/r4bPm5OavSePjUcY777wDwOabbw5A9+7dAXjppZcA2GeffYBoWRDnOdF+lePLifjoHV92KDNqlsrw9ddfA9GsWf5vTXr37g1EVyilXj5IEayISEKKEsF6rirANddcE2sfH4/ueW8+9nillVLfCX63UBoPz1n0f3MzQv79739Xu1+nTp0AuP7664Fo2RDPg33jjTcKX1gpKz5rlrcXU6ZMAWqOYD2rIOm+eEWwIiIJKUoEm5nP6PO+3nDDDQAsWLAg671nnXUWsOJ8jxdccAEQ3Qn2byppujzf1ef+9TxaH83zpz/9qSTlkuLxK1ofredZSZmzZFXH64b38SZFEayISEKKEsGuuuqqVdsdOnQAomyCe++9F4C2bdsCUYTq30w+Q7n3v8Xtw5XGy3MhfT4BH3c+evRoIJozVBq/jTbaCIC999476/lXXnml1v1ef/31xMqUSRGsiEhCihLB+nhigO233x6I5uj0lURzvfjiiwD0798fiKJgz5XcaqutgOiOszR+nk/ta3W1a9cOiKIRX49Nmg7PlXY+j4VfIddk3XXXBWDOnDlVzyWRUaAIVkQkIUWJYDP7TX2OT89Z9DHF/s3j48b//Oc/A1EWgUe8W2+9NRCNN//iiy+SLLqUAZ+TwPvr1157bSCKXIcOHQpEs3JJ0+Fzmji/Wv75559r3e9vf/sbACeddFLVc1rRQESkglg+M3ubWcGmAfd5Xn1Els+IVJMePXoA0Qqivn7TlltuWagiVQkhaJhYTIWsE7l8bgHvZ/c7xj7T0u677w5Es2glbFIIQcnXMSRZJ5yvWuF1w/NZf/nLXwIwa9asWvffd999gWieC6jXqrJ11glFsCIiCVEDKyKSkKIv2+1++OGHBu1f1yWAVC7vGvCBA9414NMN+mCUInUNSBnyG+deV/xSP2674MOrk6YIVkQkISWLYBvKJ1OWxqNVq1YAXH755UA0JHb+/PlANPl6saIPKT+rr746AAceeGDW876IarlRBCsikpCKjWAnTZpU6iJIgflSMWeeeWbW84pcxZ199tlAFMn6QKOnn366ZGWqjSJYEZGEVFwE65O9XHvttSUuiSTFBxL4Z+zDqEU8o8SNHz8eiIbUlxtFsCIiCSnZUNlypqGy8TWVOoGGysamOhFRBCsikhA1sCIiCVEDKyKSkHyzCGYBM5IoSBnpWOoCVJimUCdA9SIfqhNped3kEhGR+NRFICKSEDWwIiIJUQMrIpIQNbAiIglRAysikhA1sCIiCVEDKyKSEDWwIiIJUQMrIpKQ/we1IcLmFvpxAgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 超参\n",
    "n_epochs = 3\n",
    "batch_size_train = 64\n",
    "batch_size_test = 1000\n",
    "learning_rate = 0.01\n",
    "momentum = 0.5\n",
    "log_interval = 10\n",
    "random_seed = 42\n",
    "torch.manual_seed(random_seed)\n",
    "MINST_mean = 0.1307\n",
    "MINST_std = 0.3081\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(torchvision.datasets.MNIST(\n",
    "    './_data_set/',\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=torchvision.transforms.Compose([\n",
    "        torchvision.transforms.ToTensor(),\n",
    "        torchvision.transforms.Normalize((MINST_mean, ), (MINST_std, ))\n",
    "    ])),\n",
    "                                           batch_size=batch_size_train,\n",
    "                                           shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(torchvision.datasets.MNIST(\n",
    "    './_data_set/',\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=torchvision.transforms.Compose([\n",
    "        torchvision.transforms.ToTensor(),\n",
    "        torchvision.transforms.Normalize((MINST_mean, ), (MINST_std, ))\n",
    "    ])),\n",
    "                                          batch_size=batch_size_test,\n",
    "                                          shuffle=True)\n",
    "\n",
    "batch_idx, (example_data, example_targets) = next(enumerate(train_loader))\n",
    "# 每批次有 64 张单通道 28x28 大小的图片\n",
    "print(example_data.shape)\n",
    "# 每个图片实际的数字标签\n",
    "print(example_targets)\n",
    "\n",
    "fig = plt.figure()\n",
    "for i in range(9):\n",
    "    plt.subplot(3, 3, i + 1)\n",
    "    plt.tight_layout()\n",
    "    plt.imshow(example_data[i][0], cmap='gray', interpolation='none')\n",
    "    # plt.title(\"数字: {}\".format(example_targets[i]))\n",
    "    plt.title(f'number: {example_targets[i]}')\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_18752\\2337744027.py:29: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  return F.log_softmax(x)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.306355\n",
      "Train Epoch: 1 [640/60000 (1%)]\tLoss: 2.309732\n",
      "Train Epoch: 1 [1280/60000 (2%)]\tLoss: 2.263060\n",
      "Train Epoch: 1 [1920/60000 (3%)]\tLoss: 2.253021\n",
      "Train Epoch: 1 [2560/60000 (4%)]\tLoss: 2.239486\n",
      "Train Epoch: 1 [3200/60000 (5%)]\tLoss: 2.232780\n",
      "Train Epoch: 1 [3840/60000 (6%)]\tLoss: 2.223558\n",
      "Train Epoch: 1 [4480/60000 (7%)]\tLoss: 2.174626\n",
      "Train Epoch: 1 [5120/60000 (9%)]\tLoss: 2.122881\n",
      "Train Epoch: 1 [5760/60000 (10%)]\tLoss: 2.025848\n",
      "Train Epoch: 1 [6400/60000 (11%)]\tLoss: 1.923471\n",
      "Train Epoch: 1 [7040/60000 (12%)]\tLoss: 1.832063\n",
      "Train Epoch: 1 [7680/60000 (13%)]\tLoss: 1.906025\n",
      "Train Epoch: 1 [8320/60000 (14%)]\tLoss: 1.673950\n",
      "Train Epoch: 1 [8960/60000 (15%)]\tLoss: 1.537203\n",
      "Train Epoch: 1 [9600/60000 (16%)]\tLoss: 1.439621\n",
      "Train Epoch: 1 [10240/60000 (17%)]\tLoss: 1.275429\n",
      "Train Epoch: 1 [10880/60000 (18%)]\tLoss: 1.183242\n",
      "Train Epoch: 1 [11520/60000 (19%)]\tLoss: 1.182200\n",
      "Train Epoch: 1 [12160/60000 (20%)]\tLoss: 1.154036\n",
      "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 0.921464\n",
      "Train Epoch: 1 [13440/60000 (22%)]\tLoss: 0.877538\n",
      "Train Epoch: 1 [14080/60000 (23%)]\tLoss: 0.917646\n",
      "Train Epoch: 1 [14720/60000 (25%)]\tLoss: 0.928683\n",
      "Train Epoch: 1 [15360/60000 (26%)]\tLoss: 0.883320\n",
      "Train Epoch: 1 [16000/60000 (27%)]\tLoss: 0.829271\n",
      "Train Epoch: 1 [16640/60000 (28%)]\tLoss: 0.843372\n",
      "Train Epoch: 1 [17280/60000 (29%)]\tLoss: 0.962177\n",
      "Train Epoch: 1 [17920/60000 (30%)]\tLoss: 0.816695\n",
      "Train Epoch: 1 [18560/60000 (31%)]\tLoss: 0.803838\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss: 0.706732\n",
      "Train Epoch: 1 [19840/60000 (33%)]\tLoss: 0.697531\n",
      "Train Epoch: 1 [20480/60000 (34%)]\tLoss: 0.748919\n",
      "Train Epoch: 1 [21120/60000 (35%)]\tLoss: 0.599384\n",
      "Train Epoch: 1 [21760/60000 (36%)]\tLoss: 0.873525\n",
      "Train Epoch: 1 [22400/60000 (37%)]\tLoss: 0.730187\n",
      "Train Epoch: 1 [23040/60000 (38%)]\tLoss: 0.780188\n",
      "Train Epoch: 1 [23680/60000 (39%)]\tLoss: 0.688149\n",
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      "Train Epoch: 1 [59520/60000 (99%)]\tLoss: 0.548745\n",
      "\n",
      "Test set: Avg. loss: 0.1674, Accuracy: 9512/10000 (95%)\n",
      "\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.315766\n",
      "Train Epoch: 2 [640/60000 (1%)]\tLoss: 0.468051\n",
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      "\n",
      "Test set: Avg. loss: 0.1095, Accuracy: 9664/10000 (97%)\n",
      "\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.606901\n",
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      "Train Epoch: 3 [53120/60000 (88%)]\tLoss: 0.236363\n",
      "Train Epoch: 3 [53760/60000 (90%)]\tLoss: 0.508030\n",
      "Train Epoch: 3 [54400/60000 (91%)]\tLoss: 0.379145\n",
      "Train Epoch: 3 [55040/60000 (92%)]\tLoss: 0.304528\n",
      "Train Epoch: 3 [55680/60000 (93%)]\tLoss: 0.184158\n",
      "Train Epoch: 3 [56320/60000 (94%)]\tLoss: 0.323510\n",
      "Train Epoch: 3 [56960/60000 (95%)]\tLoss: 0.192909\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss: 0.275473\n",
      "Train Epoch: 3 [58240/60000 (97%)]\tLoss: 0.397780\n",
      "Train Epoch: 3 [58880/60000 (98%)]\tLoss: 0.159153\n",
      "Train Epoch: 3 [59520/60000 (99%)]\tLoss: 0.231473\n",
      "\n",
      "Test set: Avg. loss: 0.0909, Accuracy: 9709/10000 (97%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        # 卷积层\n",
    "        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n",
    "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
    "        # 加一层 dropout (删除神经元), 防止过拟合; 不要直接用 F.dropout2d\n",
    "        self.conv2_drop = nn.Dropout2d()\n",
    "        # 全连接层\n",
    "        self.fc1 = nn.Linear(320, 50)\n",
    "        self.fc2 = nn.Linear(50, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # 原28x28x1 -> 卷积24x24x10 -> 池化12x12x10 -> 激活12x12x10\n",
    "        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n",
    "\n",
    "        # 原12x12x10 -> 卷积8x8x20 -> 池化4x4x20 -> 激活4x4x20\n",
    "        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n",
    "        # 320 = 4x4x20 将张量x变形成一维向量形式，总特征数不变，为全连接层做准备\n",
    "        x = x.view(-1, 320)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.dropout(x, training=self.training)\n",
    "        x = self.fc2(x)\n",
    "        return F.log_softmax(x)\n",
    "\n",
    "\n",
    "network = Net()\n",
    "optimizer = optim.SGD(network.parameters(),\n",
    "                      lr=learning_rate,\n",
    "                      momentum=momentum)\n",
    "\n",
    "train_losses = []\n",
    "train_counter = []\n",
    "test_losses = []\n",
    "test_counter = [i * len(train_loader.dataset) for i in range(n_epochs + 1)]\n",
    "\n",
    "\n",
    "def train(epoch):\n",
    "    network.train()\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        optimizer.zero_grad()\n",
    "        output = network(data)\n",
    "        loss = F.nll_loss(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if batch_idx % log_interval == 0:\n",
    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "                epoch + 1, batch_idx * len(data), len(train_loader.dataset),\n",
    "                100. * batch_idx / len(train_loader), loss.item()))\n",
    "            train_losses.append(loss.item())\n",
    "            train_counter.append((batch_idx * 64) +\n",
    "                                 (epoch * len(train_loader.dataset)))\n",
    "            torch.save(network.state_dict(), './_data_set/MNIST/model.pth')\n",
    "            torch.save(optimizer.state_dict(),\n",
    "                       './_data_set/MNIST/optimizer.pth')\n",
    "\n",
    "\n",
    "def test():\n",
    "    network.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    # 测试集不需要反向传播，所以使用 torch.no_grad() 方法关闭计算图\n",
    "    with torch.no_grad():\n",
    "        for data, target in test_loader:\n",
    "            output = network(data)\n",
    "            test_loss += F.nll_loss(output, target, reduction='sum').item()\n",
    "            pred = output.data.max(1, keepdim=True)[1]\n",
    "            correct += pred.eq(target.data.view_as(pred)).sum()\n",
    "    test_loss /= len(test_loader.dataset)\n",
    "    test_losses.append(test_loss)\n",
    "    print('\\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
    "        test_loss, correct, len(test_loader.dataset),\n",
    "        100. * correct / len(test_loader.dataset)))\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    # train(0)\n",
    "    # 不加这个，后面画图就会报错：x and y must be the same size\n",
    "    test()\n",
    "    for epoch in range(n_epochs):\n",
    "        train(epoch)\n",
    "        test()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 12))\n",
    "plt.plot(train_counter, train_losses, color='blue')\n",
    "plt.scatter(test_counter, test_losses, color='red')\n",
    "plt.legend(['Train Loss', 'Test Loss'], loc='upper right')\n",
    "plt.xlabel('number of training examples seen')\n",
    "plt.ylabel('negative log likelihood loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_18752\\2337744027.py:29: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  return F.log_softmax(x)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.180916\n",
      "Train Epoch: 4 [640/60000 (1%)]\tLoss: 0.188782\n",
      "Train Epoch: 4 [1280/60000 (2%)]\tLoss: 0.250786\n",
      "Train Epoch: 4 [1920/60000 (3%)]\tLoss: 0.185484\n",
      "Train Epoch: 4 [2560/60000 (4%)]\tLoss: 0.295455\n",
      "Train Epoch: 4 [3200/60000 (5%)]\tLoss: 0.172000\n",
      "Train Epoch: 4 [3840/60000 (6%)]\tLoss: 0.117650\n",
      "Train Epoch: 4 [4480/60000 (7%)]\tLoss: 0.423349\n",
      "Train Epoch: 4 [5120/60000 (9%)]\tLoss: 0.285250\n",
      "Train Epoch: 4 [5760/60000 (10%)]\tLoss: 0.360192\n",
      "Train Epoch: 4 [6400/60000 (11%)]\tLoss: 0.362748\n",
      "Train Epoch: 4 [7040/60000 (12%)]\tLoss: 0.292238\n",
      "Train Epoch: 4 [7680/60000 (13%)]\tLoss: 0.238687\n",
      "Train Epoch: 4 [8320/60000 (14%)]\tLoss: 0.150868\n",
      "Train Epoch: 4 [8960/60000 (15%)]\tLoss: 0.427452\n",
      "Train Epoch: 4 [9600/60000 (16%)]\tLoss: 0.230040\n",
      "Train Epoch: 4 [10240/60000 (17%)]\tLoss: 0.275437\n",
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      "\n",
      "Test set: Avg. loss: 0.0696, Accuracy: 9772/10000 (98%)\n",
      "\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss: 0.270343\n",
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      "\n",
      "Test set: Avg. loss: 0.0648, Accuracy: 9794/10000 (98%)\n",
      "\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss: 0.157271\n",
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      "Train Epoch: 6 [59520/60000 (99%)]\tLoss: 0.251494\n",
      "\n",
      "Test set: Avg. loss: 0.0615, Accuracy: 9814/10000 (98%)\n",
      "\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss: 0.091176\n",
      "Train Epoch: 7 [640/60000 (1%)]\tLoss: 0.269873\n",
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      "\n",
      "Test set: Avg. loss: 0.0576, Accuracy: 9823/10000 (98%)\n",
      "\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss: 0.411129\n",
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      "\n",
      "Test set: Avg. loss: 0.0554, Accuracy: 9817/10000 (98%)\n",
      "\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss: 0.054686\n",
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      "Train Epoch: 9 [55680/60000 (93%)]\tLoss: 0.238617\n",
      "Train Epoch: 9 [56320/60000 (94%)]\tLoss: 0.195722\n",
      "Train Epoch: 9 [56960/60000 (95%)]\tLoss: 0.252661\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss: 0.149977\n",
      "Train Epoch: 9 [58240/60000 (97%)]\tLoss: 0.061803\n",
      "Train Epoch: 9 [58880/60000 (98%)]\tLoss: 0.105117\n",
      "Train Epoch: 9 [59520/60000 (99%)]\tLoss: 0.054359\n",
      "\n",
      "Test set: Avg. loss: 0.0508, Accuracy: 9843/10000 (98%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "continued_network = Net()\n",
    "continued_optimizer = optim.SGD(network.parameters(),\n",
    "                                lr=learning_rate,\n",
    "                                momentum=momentum)\n",
    "\n",
    "network_state_dict = torch.load('./_data_set/MNIST/model.pth')\n",
    "continued_network.load_state_dict(network_state_dict)\n",
    "optimizer_state_dict = torch.load('./_data_set/MNIST/optimizer.pth')\n",
    "continued_optimizer.load_state_dict(optimizer_state_dict)\n",
    "\n",
    "for i in range(3, 9):\n",
    "    train(i)\n",
    "    test()\n",
    "    test_counter.append(i * len(train_loader.dataset))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 12))\n",
    "plt.plot(train_counter, train_losses, color='blue')\n",
    "plt.scatter(test_counter, test_losses, color='red')\n",
    "plt.legend(['Train Loss', 'Test Loss'], loc='upper right')\n",
    "plt.xlabel('number of training examples seen')\n",
    "plt.ylabel('negative log likelihood loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<a>![分割线](https://cdn.jsdelivr.net/gh/Weidows/Weidows/image/divider.png)</a>\n",
    "\n",
    "## 借物表\n",
    "\n",
    "<a name='cite_note-1' href='#cite_ref-1'>[1]</a>: [用 PyTorch 实现 MNIST 手写数字识别(非常详细)](https://zhuanlan.zhihu.com/p/137571225)\n",
    "\n",
    "<a name='cite_note-2' href='#cite_ref-2'>[2]</a>: [PyTorch 深度学习入门与实战 2022 最简明易懂的 PyTorch 代码精讲 最新版本 PyTorch PyTorch 安装](https://www.bilibili.com/video/BV1cL411V7Gh?p=6)\n",
    "\n",
    "<a name='cite_note-3' href='#cite_ref-3'>[3]</a>: [PyTorch 中的 nn.Conv1d 与 nn.Conv2d](https://www.jianshu.com/p/45a26d278473)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 64-bit (system)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "8faf7328610876523a4e724188f9f8e34266025c0876869ab11d11b1ec3b5644"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
